Method for determining tactical actions

ABSTRACT

Disclosed is a method for determining tactical actions for protecting a reference entity with respect to a plurality of entities in a battlefield environment, the method including: segmenting the battlefield environment into a plurality of layers; associating actable deterrent systems with each layer; obtaining data representative of the probability, for each deterrent system, that the considered deterrent system deters an entity in the associated layer; providing, for each entity, the level of threat of the entity; and computing a cost function for determining the deterrent systems to be engaged by the reference entity for rendering extremal the cost function, the cost function being a function depending from the provided level of threat and the obtained data.

FIELD OF THE INVENTION

The present invention concerns a method for determining tactical actionsfor protecting a reference entity with respect to a plurality ofentities in a battlefield environment. The present invention alsoconcerns an associated decision support method. The present inventionalso relates to a computer program, a computer readable medium, a systemfor determining tactical actions for protecting a reference entity withrespect to a plurality of entities in a battlefield environment and adecision support system.

BACKGROUND OF THE INVENTION

To manage critical situations in a battlefield environment, accuratedecision support tools can be used. In particular, decision supportsystems can be used in a battlefield environment to help an operatordecide which battle actions to trigger, when threats are detected fromtargets in the surrounding environment of the operator. Such decisionsupport tools can use in combination different types of sensors,actuators, user interfaces and data representations.

However, no accurate decision support tools exist, the safest being torely on the knowledge of an operator.

BRIEF SUMMARY OF THE INVENTION

The invention aims at solving the problems of obtaining a reliabledecision support tool.

To this end, the invention concerns a method for determining tacticalactions for protecting a reference entity with respect to a plurality ofentities in a battlefield environment, the method comprising segmentingthe battlefield environment into a plurality of layers, associatingactable deterrent systems with each layer, obtaining data representativeof the probability, for each deterrent system, that the considereddeterrent system deters an entity in the associated layer, providing,for each entity, the level of threat of said entity, and computing acost function for determining the deterrent systems to be engaged by thereference entity for rendering extremal the cost function, the costfunction being a function depending from the provided level of threatand the obtained data.

Thanks to the invention, reliable data relative to the most favorableplan engagements can be obtained.

This enables to provide to an operator a reliable decision support toolsince it provides to the plan engagement with a chance estimation.

According to further aspects of the invention that are advantageous butnot compulsory, the method for evaluating might incorporate one orseveral of the following features, taken in any technically admissiblecombination:

-   -   at the obtaining step, data representative of the nature of the        entity are obtained.    -   at the computing step, the cost function depends at least from        the product of the provided level of threat and the obtained        data.    -   at the obtaining step, data representative of the consumption of        the considered deterrent system are obtained.    -   at the computing step, the imposed engagement policies are taken        into account.    -   the method further comprises a step of associating with each        layer a category of tactical actions that may be engaged by the        reference entity.    -   the method further comprises a step of associating at least one        geographical parameter with each layer, said parameters        comprising the distance range delimiting the layer, said        distance range being associated with a begin range and an end        range.    -   the distance range associated with each layer is predefined and        static.    -   the distance range associated with each layer is dynamically        defined depending on predefined criteria.    -   the entities and the reference entity are ships.

The invention also concerns a decision support method comprising thesteps of carrying out a method for determining tactical actions forprotecting a reference entity with respect to a plurality of entities ina battlefield environment as previously described and of generatingtactical recommendations in association with the determined deterrentsystems to be engaged.

The invention also relates to a computer program comprising instructionsfor carrying out the steps of a method as previously described when saidcomputer program is executed on a suitable computer device.

The invention also concerns a computer readable medium having encodedthereon a computer program as previously described.

The invention also relates to a system for determining tactical actionsfor protecting a reference entity with respect to a plurality ofentities in a battlefield environment, the system comprising acalculator adapted to segment the battlefield environment into aplurality of layers, and an obtaining unit adapted to obtain datarepresentative of the probability, for each deterrent system, that theconsidered deterrent system deters an entity in the associated layer,the calculator being further adapted to associate actable deterrentsystems with each layer, to provide, for each entity, the level ofthreat of said entity and to compute a cost function for determining thedeterrent systems to be engaged by the reference entity for renderingextremal the cost function, the cost function being a function dependingfrom the provided level of threat and the obtained data.

The invention also concerns a decision support system comprising asystem for evaluating as previously described, the calculator beingfurther adapted to generate tactical recommendations in association withthe determined deterrent systems to be engaged.

It is also proposed a method for evaluating the level of threat of atleast one entity among a plurality of entities in a battlefieldenvironment, the level of threat being evaluated with respect to areference entity to be protected, the method comprising the steps ofsegmenting the battlefield environment into a plurality of layers,obtaining data representative of a position of said entity with respectto the layers of the battlefield environment, and determining the levelof threat of said entity using the obtained data.

According to further aspects of the invention that are advantageous butnot compulsory, the method for evaluating might incorporate one orseveral of the following features, taken in any technically admissiblecombination:

-   -   the method further comprises a step of associating with each        layer at least one of the following: a category of tactical        actions that may be engaged by the reference entity, at least        one geographical parameter with each layer, said parameter(s)        comprising the distance range delimiting the layer, said        distance range being associated with a begin range and an end        range.    -   the method further comprises a step of associating at least one        geographical parameter with each layer, said parameter(s)        comprising the distance range delimiting the layer, said        distance range being associated with a begin range and an end        range, the distance range associated with each layer being        either predefined and static or dynamically defined for each        entity of the plurality of entities depending on predefined        criteria.    -   at the obtaining step, data representative of the behavior of        said entity are also obtained, the data representative of the        behavior including data relative to the kinematics of said        entity and data representative of the identity of said entity        with relation to the reference entity.    -   at the obtaining step, data representative of the dangerousness        of said entity are also obtained.    -   at the obtaining step, data representative of the urgency of the        potential threat represented by said entity and/or data        representative of the capability to engage and deter or kill the        potential threat represented by said entity are also obtained.    -   at the determining step, a machine learning algorithm is        applied.    -   at the determining step, the obtained data are aggregated using        a Choquet integral and/or a generalized additive independence        model.    -   the entities and the reference entity are ships.

The invention also concerns a decision support method comprising thesteps of carrying out a method for evaluating the level of threat of atleast one entity among a plurality of entities in a battlefieldenvironment as previously described, generating tactical recommendationsin association with said entity depending on the determined level ofthreat.

According to a specific embodiment, the decision support method iscarried out iteratively.

The invention also relates to a computer program comprising instructionsfor carrying out the steps of a method as previously described when saidcomputer program is executed on a suitable computer device.

The invention also concerns a computer readable medium having encodedthereon a computer program as previously described.

The invention also relates to a system for evaluating the level ofthreat of at least one entity among a plurality of entities in abattlefield environment, the level of threat being evaluated withrespect to a reference entity to be protected, the system comprising acalculator adapted to segment the battlefield environment into aplurality of layers, and an obtaining unit adapted to obtain datarepresentative of a position of said entity with respect to the layersof the battlefield environment, the calculator being further adapted todetermine the level of threat of said entity using the obtained data.

The invention also concerns a decision support system comprising asystem for evaluating as previously described, the calculator beingfurther adapted to generate tactical recommendations in association withsaid entity depending on the determined level of threat.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood on the basis of the followingdescription, which is given in correspondence with the annexed figuresand as an illustrative example, without restricting the object of theinvention. In the annexed figures:

FIG. 1 is a schematic representation of a system and a computer programproduct, whose interaction enables to carry out a method,

FIG. 2 is a flowchart of an example of carrying out of a method forevaluating the level of threat comprising a step of determining thelevel of threat,

FIG. 3 is a schematic representation of a segmented environment,

FIG. 4 is a flowchart of an example of carrying out the step ofdetermining the level of threat of the method for evaluating the levelof threat illustrated by FIG. 2,

FIG. 5 is a flowchart of an example of carrying out of a method fordetermining tactical actions for protecting a reference entity, and

FIG. 6 is a schematic representation of a decision support system.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

A system 10 and a computer program product 12 are represented in FIG. 1.The interaction between the computer program product 12 and the system10 enables to carry out a method.

System 10 is a computer. In the present case, system 10 is a laptop.

More generally, system 10 is a computer or computing system, or similarelectronic computing device adapted to manipulate and/or transform datarepresented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

System 10 comprises a processor 14, a keyboard 22 and a display unit 24.

The processor 14 comprises a data-processing unit 16, memories 18 and areader 20 adapted to read a computer readable medium.

The computer program product 12 comprises a computer readable medium.

The computer readable medium is a medium that can be read by the readerof the processor. The computer readable medium is a medium suitable forstoring electronic instructions, and capable of being coupled to acomputer system bus.

Such computer readable storage medium is, for instance, a disk, a floppydisks, optical disks, CD-ROMs, magnetic-optical disks, read-onlymemories (ROMs), random access memories (RAMs) electrically programmableread-only memories (EPROMs), electrically erasable and programmable readonly memories (EEPROMs), magnetic or optical cards, or any other type ofmedia suitable for storing electronic instructions, and capable of beingcoupled to a computer system bus.

A computer program is stored in the computer readable storage medium.The computer program comprises one or more stored sequence of programinstructions.

The computer program is loadable into the data-processing unit 16 andadapted to cause execution of a method is run by the data-processingunit 16.

Operation of the system 10 is now described in reference to theflowchart of FIG. 2, which illustrates an example of carrying out amethod for evaluating the level of threat of at least one entity among aplurality of entities in a battlefield environment. In the remainder ofthe description, such method is labeled “a method for evaluating”. Theone entity considered for which the level of threat is to be estimatedis the suspicious entity. Such suspicious entity is labeled SE in theremainder of the description.

The level of threat is evaluated with respect to a reference entity REto be protected.

According to a preferred embodiment, each suspicious entity SE is a shipand the reference entity RE is also a ship.

The number of suspicious entities SE depends on the operationalsituation and varies over time.

According to an embodiment, the number of suspicious entities SE is 0,which is the case if there is no activity around the reference entityRE.

Alternatively, the number of suspicious entity SE is superior or equalto 5.

According to another embodiment, the number of suspicious entities SE issuperior or equal to 50 if the range of the area under surveillance islarge and there is a lot of civilian activity (fishing boats notably).

The method for evaluating comprises four steps: a segmenting step S10,an associating step S20, an obtaining step S30 and a determining stepS40.

At the segmenting step S10, the battlefield environment is segmentedinto a plurality of layers.

The segmentation of the environment which results from the segmentingstep S10 is represented on FIG. 3.

It appears that the environment of the reference entity RE is separatedin five layers which are from the closest to the furthest from thereference entity RE: a first layer L1, a second layer L2, a third layerL3, a fourth layer L4 and a fifth layer L5.

Each layer is delimitated by at least one circle so that the first layerL1 has the shape of disk whereas the other layers L2, L3, L4 and L5 havean annular shape.

Alternatively, each layer is delimitated by more complex shape ofboundary. This is in particular the case if the suspicious ship SE isclose of a shore. The boundary may be distorted in the direction of theshore.

At the associating step S20, each layer L1, L2, L3, L4 and L5 isassociated to a category of tactical actions that may be engaged by thereference entity RE.

According to the illustrated example, each layer L1, L2, L3, L4 and L5is associated to the main operational mission to be fulfilled.

In the example, the first layer L1 is the closest to the referenceentity RE. When a suspicious entity SE is this close from the referenceentity RE, the reference entity RE cannot use its weaponry, only thecrew can defend themselves. For this reason, the first layer L1 is alsonamed the “no capacity layer”.

For the second layer L2, the reference entity RE can use lethaleffectors and actively engage suspicious entities SE. For this reason,the second layer L2 is also named the “engage layer”.

In the third layer L3, the reference entity RE is entitled to usenon-lethal effectors to try to actively discourage suspicious entitiesSE from engaging or coming closer. Therefore, the third layer L3 is alsonamed the “deter layer”.

In the fourth layer L4, soft, information bearing effectors can be usedby the reference entity RE to warn enemy entities. Thus, the fourthlayer L4 is also named the “warn layer”.

For a potential enemy entity in the fifth layer L5, only identificationactions can be performed, no effector, hard or soft, may be used. Forthis reason, the fifth layer L5 is also named the “identify layer”.

Alternatively, at the associating step S20, at least one geographicalparameter is associated with each layer L1, L2, L3, L4 and L5.

As an example, a parameter is the distance range delimiting a layer L1,L2, L3, L4 and L5, said distance range being associated with a beginrange and an end range.

According to an embodiment, the distance range associated with eachlayer L1, L2, L3, L4 and L5 is predefined and static.

According to another embodiment, the distance range associated with eachlayer L1, L2, L3, L4 and L5 is dynamically defined depending onpredefined criteria. For instance, the distance range can dynamicallyvary over time, depending on at least one criterion chosen among thefollowing list: tactical scenarios, threat levels, risk mitigationlevels, enemy entities capabilities, offensive means of the referenceentity RE and defensive means of the reference entity RE.

At the obtaining step S30, data representative of a position of saidsuspicious entity SE with respect to the layers L1, L2, L3, L4 and L5 ofthe battlefield environment are obtained.

According to the illustrated method for evaluating, data representativeof the trajectory history information for said entity over the differentlayers L1, L2, L3, L4 and L5 are also obtained at the obtaining stepS30.

According to the illustrated method for evaluating, at the obtainingstep S30, data representative of the speed of said suspicious entity SE,the heading angle of said suspicious entity SE and the closest point ofapproach of said suspicious entity SE with relation to the referenceentity RE are also obtained.

The direction of the suspicious entity SE is a projection of its headingangle with respect to the reference entity RE. To express thisdirection, the bearing to the suspicious entity RE and the heading ofthe suspicious entity SE are used.

The bearing to the suspicious entity SE is given by a sensor. It is theangle at which the suspicious entity SE is considering the headingreference entity RE as pointing to 0 degree.

The closest point of approach is notably expressed in terms of distance.Such distance is labeled closest point of approach distance d_(CPA).

The closest point of approach distance d_(CPA) uses the current speedsand positions of the suspicious entity SE and of the reference entitiesRE. Each value is obtained by using a sensor.

A common formula to calculate the closest point of approach distanced_(CPA) is to calculate first the closest point of approach time andthen to derive a distance.

The closest point of approach time is the time at which two boats willbe at the closest point.

The distance between two points identified by their latitude andlongitude can be obtained by using the Haversine or Vincenty's formulae.

The Haversine formula is an equation important in navigation, givinggreat-circle distances between two points on a sphere from theirlongitudes and latitudes. It is a special case of a more general formulain spherical trigonometry, the law of haversines, relating the sides andangles of spherical triangles.

Preferably, the distance between two points identified by their latitudeand longitude can be obtained by using the Vincenty's formulae.

Vincenty's formulae are two related iterative methods used in geodesy tocalculate the distance between two points on the surface of a spheroid,developed by Thaddeus Vincenty (1975). These formulae are based on theassumption that the figure of the Earth is an oblate spheroid, and henceare more accurate than methods such as great-circle distance whichassume a spherical Earth. The first (direct) method computes thelocation of a point which is a given distance and azimuth (direction)from another point. The second (inverse) method computes thegeographical distance and azimuth between two given points. Both methodshave been widely used in geodesy because they are accurate to within 0.5mm (0.020″) on the Earth ellipsoid.

According to the illustrated method for evaluating, at the obtainingstep S30, data related to a change in the layer L1, L2, L3, L4 and L5 towhich the suspicious entity SE belongs is obtained.

According to the illustrated method for evaluating, at the obtainingstep S30, data representative of the dangerousness of said suspiciousentity SE are also obtained.

The dangerousness helps to quantify the worst impact the suspiciousentity SE can have on the reference entity RE, based on the effectors onboard of the suspicious entity SE. Data representative of thedangerousness can be directly a scale measuring the intensity ofdamages, or more indirectly the type of effector (gun, bomb or rocket).The type of effector can be entered manually by an operator or providedby the system using sensor information. By default, some pre-definedeffectors can be assigned to categories of boats.

According to the illustrated method for evaluating, at the obtainingstep S30, data representative of the identity of the suspicious entitySE are also obtained.

For example, the identity of the suspicious entity SE is enteredmanually by an operator, notably by using the keyboard 22.

Data representative of the identity can be constructed automaticallyfrom pre-defined rules. For instance, a suspicious entity SE that isconsidered as “neutral” and enters the engage layer (second layer L2)can be automatically considered as “hostile”.

According to the illustrated method for evaluating, at the obtainingstep S30, data representative of the urgency of the potential threatrepresented by the suspicious entity SE are also obtained.

By definition, the urgency takes into account the time at which thesuspicious entity SE can engage the reference entity RE and the timeuntil which the reference entity RE can engage the suspicious entity SE.

According to the illustrated method for evaluating, at the obtainingstep S30, data representative of the group impact of the plurality ofentities on the suspicious entity SE are also obtained.

Data representative of the group impact is provided manually by anoperator which identifies groups of entities that realize a coordinatedaction. It can also be provided by the system using sensor informationto correlate the behavior of two or more suspicious entities SE andallocate them a group identifier.

At the determining step S40, the level of threat of the suspiciousentity SE using the obtained data is determined.

At the determining step S40, the obtained data are aggregated usingpossibly two decision models. The first one is a Choquet integral andthe second one is the Generalized Additive Independence (GAI) model.

Choquet integral is a subadditive or superadditive integral created bythe French mathematician Gustave Choquet in 1953. It was initially usedin statistical mechanics and potential theory, but found its way intodecision theory in the 1980s, where it is used as a way of measuring theexpected utility of an uncertain event. It is applied specifically tomembership functions and capacities. In imprecise probability theory,the Choquet integral is also used to calculate the lower expectationinduced by a 2-monotone lower probability, or the upper expectationinduced by a 2-alternating upper probability. The Choquet integral hasbeen applied to multi-criteria decision analysis in the 1990s. Its mainasset in this context is its ability to represent complex decisionstrategies such as veto criteria, favor criteria, synergies amongcriteria and redundancy among criteria to cite a few.

The Generalized Additive Independence (GAI) model has been introduced byPeter C. Fishburn in 1967 as a generalization of additive utility inmulti-attribute utility theory. It did not receive much attention atthat time. Its importance arises from the Artificial Intelligencecommunity with the work of F. Bacchus and A. Grove in 1995. Since the2000s, this model is recognized as a relevant model for representingpreferences in a compact way (not storing the utility for potentialalternative) while being able to represent any kind of interaction amongthe attributes.

In addition or alternatively, at the determining step S40, the obtaineddata are aggregated using either a Choquet integral, a GAI model, or acombination of both.

Preferably, the GAI model is used to obtain an ordering (according tothe threat level) between the obtained data relatively to some point ofview, for instance the kinematics criteria. Then, the Choquet integralis used to aggregate the output of the GAI model with other obtaineddata, representing other points of view.

As a specific example, at the determining step, several models are usedsimultaneously as schematically illustrated by the flowchart of FIG. 4.

According to the example of FIG. 4, fifth models are used: a first modelA, a second model A′, a third model B, a fourth model C and a fifthmodel D.

The first model A takes into account kinematic criteria.

According to the example of FIG. 4, the first model A takes into accountthe speed of said suspicious entity SE, the heading angle of saidsuspicious entity SE and the closest point of approach of saidsuspicious entity SE.

The use of the first model A is to introduce new kinematic parametersfrom the three parameters just previously mentioned, in order tointegrate expertise on monotonicity relation between the inputparameters and the evolution of the threat level.

The “CPA” parameter that is an output of model A basically indicatesthat the smaller the closest point of approach distance, the higher thethreat level. The “Heading angle” parameter that is an output of model Ais 1+cos(θ)/2, where θ is the heading angle. It indicates that thethreat level is larger if the suspicious entity SE is pointing towardsthe reference entity RE.

The monotonicity regarding the mean speed parameter is a little bit morecomplicated and can be decomposed into two separate criteria: the“Incoming speed” parameter and the “outgoing speed” parameter.

The “Incoming speed” parameter is an output of model A expressing thefact that the higher the speed, the larger the threat level, when thesaid suspicious entity SE is pointing towards the reference entity RE.

The “outgoing speed” parameter is an output of model A expressing thefact that the larger the speed, the smaller the threat level, when thesaid suspicious entity SE is pointing in the opposite direction to thereference entity RE.

In other words, in general terms, model A transforms the basic kinematicparameters in order to ease their aggregation in model B.

The second model A′ also takes into account position criteria.

According to the example of FIG. 4, the second model A′ takes intoaccount the distance of said suspicious entity SE.

The use of the second model A′ enables to obtain additional kinematicdata relative to the layer L1, L2, L3, L4 and L5 to which the suspiciousentity SE belongs and/or will belong, and also the distance of the saidsuspicious entity SE to the boundaries of the considered layer.

The third model B carries out qualitative kinematics treatment based onthe data calculated by the first model A and by the second model A′, ondata relative to the layer, the change of layer, the CPA distance, theincoming and outgoing speed and the heading angle of said suspiciousentity SE.

The threat level function is calibrated from training instances(examples of suspicious entities for which we only know the values ofthe representative data) that are rated by experts (in terms of theirthreat level).

Model B only aims at representing the qualitative part of the traininginstances, that is model B is learnt only to rank order the traininginstances in the correct way. A GAI model is used in this layer.

The fourth model C carries out quantitative kinematics treatment basedon the output data of model B.

Model B does not return the correct threat level. Model B rather returnsa threat level that enables ranking the suspicious entities SE in thecorrect way.

Model C is then used to modify the qualitative score on model B in orderto represent not only the correct orderings but also the correct ratesof the training instances. Model C is just a simple function taking asargument the output of model B.

Models A, B and C focus only on the kinematics part of the parameters.The output of model C is a threat level that takes into account allparameters related to the kinematics of the suspicious entity SE.

The fifth model D determines the overall threat level based on theoutput data of model C, the dangerousness of said suspicious entity SE,the identity of the suspicious entity SE, the urgency of the potentialthreat represented by the suspicious entity SE and the group impact ofthe plurality of entities on the suspicious entity SE.

Model D returns a threat level that integrates all aspects of thethreat. It is thus the overall threat level of the said suspiciousentity SE that is presented to the operator. A Choquet integral is usedin the aggregation function of model D.

As explained in accordance with the flowchart of FIG. 2, the method forevaluating takes into account multiple relevant criteria. Moreprecisely, in the described context, the threat assessment relies on arisk analysis using the criteria for each weapon a track has on board.For instance, the following criteria are used:

-   -   group impact: this criterion evaluates the impact of a group        according to the group size of the plurality of entities and the        layer position of the track. A suspicious entity SE is more        threatening if it belongs to a group, all other parameters being        equal.    -   Dangerousness: this criterion evaluates the worst impact the        suspicious entity SE can produce on the reference entity RE. It        depends on the effectors on board of the suspicious entity SE.    -   Urgency: urgency aggregates two complementary criteria: target        effector ranges and the effector range of the reference entity        RE. Target effector range expresses the time (in seconds) before        the suspicious entity SE can engage the reference entity RE. It        is computed using the target weapon range; the lower this time        is the higher the threat is. The weight is higher to give the        priority to the survivability of the navy ship. The effector        range of the reference entity RE expresses the time (in seconds)        before the reference entity can engage the suspicious entity SE.        It is computed using the current speed of the track and the        distance to hard kill layer. The lower this time is, the higher        the threat is (capturing the lack of margin to take a decision).    -   Identity: the identity of the track (friend, hostile . . .        etc.), pending is considered as hostile. There is a veto on this        criterion: if the track is a friend the global threat level is        0.    -   CPA distance: if the CPA distance is 0 then the threat is        maximal, threat level varies according to the distance.    -   layer position: the threat level increases according to the        layer (for instance, engage layer is associated to a utility of        1, etc.). It can be refined by using the distance between the        suspicious entity SE and the reference entity RE. The smaller        the distance, the higher the threat level.    -   change in layer position: if the layer number decreases, the        target boat is coming closer to the reference entity RE, hence        its threat level is maximal.    -   speed direction: this criterion capture the speed according to        the heading angle If the suspicious entity SE is not heading to        the reference entity RE the speed has a negative value otherwise        it has a positive value.    -   heading angle: it indicates the direction of the speed vector of        the suspicious entity SE with respect to the position of the        reference entity RE.

Thus, reliable data relative to the level of threat of at least oneentity among a plurality of entities in a battlefield environment can beobtained.

This enables to provide to an operator a reliable decision support toolsince evaluating the threat is the first step towards deciding anengagement plan.

Another operation of the system 10 is now described in reference to theflowchart of FIG. 5, which illustrates an example of carrying out amethod for determining tactical actions for protecting the referenceentity RE with respect to a plurality of entities in a battlefieldenvironment.

The method for determining comprises four steps: a segmenting step S110,an associating step S120, an obtaining step S130, a providing step S140and a computing step S140.

The same remarks made for the segmenting step S10 for the method forevaluating apply for the segmenting step S110 of the method fordetermining.

At the associating step S120, each layer L1, L2, L3, L4 and L5 isassociated to actable deterrent systems. As previously explained, fivelayers are considered as an example, being understood that other numberof layers may be considered.

For example, actable deterrent systems are a gun, a long range acousticdevice (also known under the acronym LRAD), a radio, a laser adapted toemit intimidating spotlights and/or to dazzle a suspicious entity SE, aradio or a horn.

The same remarks made for the segmenting step S120 for the method fordetermining apply for the associating step S120 of the method fordetermining.

At the obtaining step S130, data representative of the probability, foreach deterrent system, that the considered deterrent system deters asuspicious entity SE in the associated layer L1, L2, L3, L4 and L5 isobtained.

Such probabilities are named hit probabilities.

According to an embodiment, hit probabilities are expert definedfunctions which act as estimators of the probability for a given weaponor effector to actually hit its target and/or have the intended effect.As of now, it simply combines individual weapons' effectivenessdiminishing with range (hit probabilities stricto sensu) together withmaximum allowed firing angle to form “complete” hit probabilities. Inother terms, if a track is situated outside the weapon's firing “cone”,even within firing range, the “complete” hit probability will be 0.Otherwise, it will have a floating point probability, between 0 (low)and 1 (high) decreasing with range, eventually hitting 0 if the track'sposition exceeds the maximum firing range. In pragmatic terms, choosingan action with a high hit probability is good because there is a strongchance the corresponding weapon or effector will hit the targeted trackhard, therefore decreasing the overall implicit threat level of thesuspicious entity SE.

According to another embodiment, the hit probabilities also depend fromthe nature of the suspicious entity SE.

The nature is linked to the category to which the suspicious entity SEbelongs. For instance, the category is a drone, a go-fast, a fast patrolboat or a jetski.

Applied to the hit probabilities, this notably means that the gun mayhave a higher hit probability on a small boat than on a high boat. Insuch context, the hit probability is should rather be construed as aprobability of success than a pure hit probability. In thisspecification, the expression “hit probability” encompasses bothmeanings.

At the obtaining step S130, data representative of the nature of thesuspicious entity SE are obtained.

Alternatively, at the obtaining step S130, data representative of theconsumption of the considered deterrent system are obtained.

The same remarks made for the obtaining step S30 for the method forevaluating may also apply for the obtaining step S130 of the method fordetermining.

At the providing step S140, for each entity, the level of threat of saidsuspicious entity SE is provided.

Such providing step S140 may be carried out by carrying out the methodfor evaluating illustrated by the flowchart of FIG. 2.

At the computing step S150, a cost function C is computed fordetermining the deterrent systems to be engaged by the reference entityRE for rendering extremal the cost function C. By this sentence, itshould be understood that there is a cost function C, that this costfunction C is evaluated for several points of evaluation and that partof the computing step results in a navigation from one evaluation pointto another evaluation point.

In addition, it is to be noted that the meaning of cost function C isconstrued in a broad manner. Generally a difference is made between acost function and an objective function according to the objective,rendering maximal or minimal. In this invention, a cost function C is tobe understood as meaning a cost function in a restricted manner or anobjective function.

The cost function C is a function depending from the provided level ofthreat and the obtained data.

According to a specific embodiment, the cost function of an engagementplan at the computing step S150 is the sum over all suspicious entitiesSE of the product of the level of threat of the suspicious entity SE bythe probability to hit that suspicious entity SE with the effectorallocated to this suspicious entity SE in the engagement plan.

Computing the cost function C may results in rendering maximal profit,vehicle flows, coverage or impact.

Computing the cost function C may also results in rendering minimalcosts, delays or fuel consumption.

For determining a maximum or a minimum for the cost function C, anexhaustive tree enumeration of each tactical action can be used.

Alternatively, greedy heuristics may be used to obtain rapidly anextremum for the cost function C.

According to a specific embodiment, at the computing step S150, the costfunction C also depends from imposed engagement policies.

For instance, a gun can only be used in the second layer L2 whereas, inthe third layer L3, the long range acoustic devices and the laser in adazzling configuration should be used. In the fourth layer, it may beconsidered to use mild broadcasting sound-based effectors such as thehorn emitting a strong deterring noise or the radio airing warningmessages aimed at suspicious entities SE. These imposed engagementpolicies ensure a gradual response to the level of threat of asuspicious entity SE.

Thanks to the invention, reliable data to help building a goodengagement plan can be obtained.

This enables to provide an operator with a reliable decision supporttool.

The method for evaluating and the method for determining may be carriedout by a decision support system 200 as represented on FIG. 6.

The decision support system 200 comprises an obtaining unit 202 and acalculator 204.

The obtaining unit 202 is adapted to obtain data representative of aposition of said entity SE with respect to the layers L1, L2, L3, L4 andL5 of the battlefield environment.

The obtaining unit 202 is a unit adapted to obtain data representativeof the probability, for each deterrent system, that the considereddeterrent system deters an suspicious entity SE in a layer L1, L2, L3,L4 and L5.

The calculator 204 is adapted to segment the battlefield environmentinto a plurality of layers L1, L2, L3, L4 and L5.

The calculator 204 is further adapted to determine the level of threatof said suspicious entity SE using the obtained data.

The calculator 204 is also adapted to associate actable deterrentsystems with each layer L1, L2, L3, L4 and L5, to provide, for eachsuspicious entity SE, the level of threat of said suspicious entity SEand to compute the cost function C for determining the deterrent systemsto be engaged by the reference entity RE for rendering extremal the costfunction C, the cost function C being a function depending from theprovided level of threat and the obtained data.

In such embodiment, the combination of the obtaining unit 202 and thecalculator 204 is a system for determining tactical actions forprotecting the reference entity RE with respect to a plurality ofentities in a battlefield environment and a system for evaluating thelevel of threat of at least one suspicious entity SE among a pluralityof entities in a battlefield environment.

Furthermore, the decision support system 200 is adapted to carry out adecision support method comprising the steps of carrying out the methodfor evaluating and generating tactical recommendations in associationwith said suspicious entity SE depending on the determined level ofthreat.

In addition, such decision support system 200 is adapted to carry out adecision support method, the decision support method comprising thesteps of carrying out the method for determining and of generatingtactical recommendations in association with the determined deterrentsystems to be engaged.

In each embodiment, it appears that the invention targets the protectionof navy vessels in piracy zones. In such zones, pirates or enemy shipssometimes launch swarm attacks on allied vessels. Allied militaryvessels have the mission to protect civil ships, in particularcommercial ones. To this end, defense layers are defined prior to themission on the basis of the worst case enemy weapon range and associatedrules of engagement are defined to state what weapons or effectors maybe used against what particular enemy track. The invention described inthe present patent application aims at solving two computationalproblems in this context: perform a multi-criteria assessment of thesituation's threat level and procure an optimized engagement planaccordingly.

The provided solution may apply in other context, for instance for thedefense of a site where physical layers can also be defined

According to a specific embodiment, several threats t1, t2, . . . , tk,. . . (k being an integer) are provided. The initial threat level forthreat tk is u(tk). The initial threat level for threat tk measures theconsequence of the realization of the effect of tk on the reference shipRE, combined with the probability that such threat tk realizes thiseffect.

Each action corresponds to a counter-measure. We are interested in theeffect of the action (the counter-measure). The probability that threattk “reacts positively” to the counter-measure if this action is producedon tk is denoted P(+cm; tk). The meaning of the expression “reactpositively” depends on the nature of the action. As an illustration, itis meant that the threat is destroyed or neutralized if the action is ahard action, that the threat is stopped if the action is a dissuasionaction, and that the threat leaves if the action is a soft action (hornfor instance).

Then the “threat level after the engagement plan” for threat tk is, whenan action is performed on the threat:U(t_k|+cm)×P(+cm;t _(13_) k)+U(t_k|−cm)×P(−cm;t_k)

Where:

-   -   U(t_k|+cm) is the utility of the threat tk if the threat tk        reacts positively to action cm (by definition,        0≤U(t_k|+cm)<U(t_k)),    -   U(t_k|−cm) is the utility of the threat tk if the threat reacts        negatively to action cm (we have U(t_k|−cm)U(t_k)), and    -   P(+cm;t_k)+P(−cm;t_k)=1.

If no action is performed in the threat, its “threat level after theengagement plan” is equal to U(t_k).

In an embodiment, the threat level is updated after the optimalengagement planning is computed. In such embodiment, the decisionsupport method is carried out iteratively. This means that the capacityto engage is used in the step of determining the threat as described inthe following paragraph.

For a suspicious entity SE on which an action is produced, the threatlevel is obtained as the addition of the utility of said suspiciousentity SE if said suspicious entity SE reacts positively to the actionmultiplied by the probability that said suspicious entity SE reactspositively, with the utility of the said suspicious entity SE if saidsuspicious entity SE reacts negatively to the action multiplied by theprobability that the suspicious entity SE reacts negatively. If theengagement plan does not produce any action to a suspicious entity SE,the threat level is not updated.

The embodiments and alternative embodiments considered here-above can becombined to generate further embodiments of the invention.

The invention claimed is:
 1. A method for determining tactical actionsfor protecting a reference entity with respect to a plurality ofentities in a battlefield environment, the method comprising: segmentingthe battlefield environment into a plurality of layers around thereference entity, associating actable deterrent systems with each layer,obtaining data representative of the probability, for each deterrentsystem, that a considered deterrent system deters an entity in anassociated layer, wherein said obtaining comprises obtaining firstkinematic parameters relative to each of said plurality of entities,providing, for each entity, a level of threat of said entity, whereinsaid providing comprises computing second kinematic parameters from saidfirst kinematic parameters, for each of said plurality of entities, saidsecond kinematic parameters presenting a monotonic relationship withregards to the level of threat, aggregating the second kinematicparameters to obtain a threat level function giving a first level ofthreat, calibrating the threat level function with a plurality oftraining instances to obtain a plurality of calibrated threat levels,and computing a second threat level using the first threat level and thecalibrated threat levels, and computing a cost function for determiningthe deterrent systems to be engaged by the reference entity forrendering extremal the cost function, the cost function being a functiondepending from the level of threat and the obtained data.
 2. The methodfor determining according to claim 1, wherein, at the obtaining step,data representative of a nature of the entity are obtained.
 3. Themethod for determining according to claim 1, wherein, at the computingstep, the cost function depends at least from the product of theprovided level of threat and the obtained data.
 4. The method fordetermining according to claim 1, wherein at the obtaining step, datarepresentative of the consumption of the considered deterrent system areobtained.
 5. The method for determining according to claim 1, wherein atthe computing step, imposed engagement policies are taken into account.6. The method for determining according to claim 1, wherein the methodfurther comprises a step of associating with each layer a category oftactical actions that may be engaged by the reference entity.
 7. Themethod for determining according to claim 1, wherein the method furthercomprises a step of associating at least one geographical parameter witheach layer, said parameters comprising the distance range delimiting thelayer, said distance range being associated with a begin range and anend range.
 8. The method for determining according to claim 1, whereinthe distance range associated with each layer is predefined and static.9. The method for determining according to claim 1, wherein the distancerange associated with each layer is dynamically defined depending onpredefined criteria.
 10. The method for determining according to claim1, wherein the entities and the reference entity are ships.
 11. Adecision support method comprising the steps of: carrying out a methodfor determining tactical actions for protecting a reference entity withrespect to a plurality of entities in a battlefield environmentaccording to claim 1, generating tactical recommendations in associationwith the determined deterrent systems to be engaged.
 12. A computerprogram comprising instructions for carrying out the steps of a methodaccording to claim 1 when said computer program is executed on asuitable computer device.
 13. A computer readable medium having encodedthereon a computer program according to claim
 12. 14. The method fordetermining according to claim 1, wherein, calibrating the threat levelcomprises a qualitative expert rating of the calibrated threat levels.15. A system for determining tactical actions for protecting a referenceentity with respect to a plurality of entities in a battlefieldenvironment, the system comprising: a calculator that segments thebattlefield environment into a plurality of layers around the referenceentity, and an obtaining unit adapted to obtain data representative of aprobability, for each deterrent system, that the considered deterrentsystem deters an entity in the associated layer, wherein said obtainingunit obtains first kinematic parameters relative to each of saidplurality of entities, the calculator being further adapted to associateactable deterrent systems with each layer, to provide, for each entity,a level of threat of said entity and to compute a cost function fordetermining the deterrent systems to be engaged by the reference entityfor rendering extremal the cost function, the cost function being afunction depending from the level of threat and the obtained data,wherein the calculator further computes second kinematic parameters fromsaid first kinematic parameters, for each of said plurality of entities,said second kinematic parameters presenting a monotonic relationshipwith regards to the level of threat, the calculator further aggregatingthe second kinematic parameters to obtain a threat level function givinga first level of threat, calibrating the threat level function with aplurality of training instances to obtain a plurality of calibratedthreat levels, and computing a second threat level using the firstthreat level and the calibrated threat levels.
 16. A decision supportsystem comprising a system for evaluating according to claim 15, thecalculator being further adapted to generate tactical recommendations inassociation with the determined deterrent systems to be engaged.